How would you design a data pipeline to ingest high‑frequency tick data for backtesting?

To ingest high‑frequency tick data for backtesting, I would build a real‑time pipeline that starts with an event‑driven ingestion layer. Kafka is ideal for capturing the raw market feed because it guarantees ordering, durability, and horizontal scalability. Each tick is published to a dedicated topic, and a Spark Structured Streaming job consumes the stream in micro‑batches. The job aggregates ticks into OHLCV candles at the desired resolution (e.g., 1‑second, 1‑minute) using windowed aggregations. After aggregation, the data is written to S3 in Parquet format, partitioned by date and symbol to enable efficient pruning during backtests. Parquet’s columnar layout and built‑in compression reduce storage costs and speed up read times. For fault tolerance, the Spark job checkpoints its state to S3, and the Kafka consumer group offsets are stored in Zookeeper or Kafka itself. The resulting dataset can be queried by analytical engines (e.g., Athena, Redshift, or a downstream Spark batch job) to run backtests. Complexity is linear in the number of ticks, O(n), and the architecture scales horizontally by adding more Kafka brokers or Spark executors as data volume grows.